The complex connectivity of neural networks is a major obstacle to understanding their organization and function. We use computational modeling, machine learning, statistics and causality to address this challenge.

In the case of the mamalian brain, strong recurrent and bidirectional connectivity characterizes the circuits, leading to a complex dynamics where various modules cooperate at different levels to give rise, for example, to coherent behaviors and percepts. This complexity manifests itself as collective oscillations as well as more complex dymamical patterns such as Sharp-wave ripple complexes, that can be observed in electrical brain activity. These neural events are believed to play a key role in information processing, learning and behavior. Our research aims at designing better techniques to detect these events and understand their underlying mechanisms and computational role.

In order to address these questions, we put an emphasis on developing new Machine learning tools with strong theoretical foundations that are particularly well suited to capture the complexity of Biological signals.
These include unsupervised learning algorithms to identify relevant patterns in large neural recording datasets, non-parametric statistical tools (based on kernel methods) to identify the complex statistical dependencies of biological signals, as well as causal inference methods to infer the underlying mechanisms generating the data.

Importantly, our work also leads us to use models to investigate the principles underlying learning and plasticity in biological and artificial networks.

In parallel, the current intensive development of new artificial deep neural networks has lead to impressive successes, but the functioning of these architectures remains largely elusive due to an aspect shared with biological networks: their high dimensional connectivity.
This provides us an opportunity to use our network analysis tools to uncover fundamental principles for such systems, and possibly relate them to biology. We are currently investigating causality and invariance principles to understand the structure of deep generative models and in particular assess their modularity.

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